RT info:eu-repo/semantics/article T1 Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network A1 García Ordás, María Teresa A1 Alaiz Moretón, Héctor A1 Benítez Andrades, José Alberto A1 García-Rodríguez, Isaías A1 García-Olalla, Oscar A1 Benavides Cuéllar, María del Carmen A2 Ingenieria de Sistemas y Automatica K1 Ingeniería de sistemas K1 Sentiment analysis K1 Fully convolutional network K1 Real time K1 MFCC K1 Mel spectrograms K1 3304.17 Sistemas en Tiempo Real AB [EN] In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS and TESS. The results obtained were promising, outperforming the state-of–the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations or financial brokers. PB Elsevier SN 1746-8094 LK http://hdl.handle.net/10612/14182 UL http://hdl.handle.net/10612/14182 NO García-Ordás, M. T., Alaiz-Moretón, H., Benítez-Andrades, J. A., García-Rodríguez, I., García-Olalla, O., & Benavides, C. (2021). Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network. Biomedical Signal Processing and Control, 69. https://doi.org/10.1016/J.BSPC.2021.102946 DS BULERIA. Repositorio Institucional de la Universidad de León RD 29-mar-2024